{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import pandas_profiling as pp \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7750.000000</td>\n",
       "      <td>7682.000000</td>\n",
       "      <td>7682.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7677.000000</td>\n",
       "      <td>7752.000000</td>\n",
       "      <td>7752.000000</td>\n",
       "      <td>7752.000000</td>\n",
       "      <td>7752.000000</td>\n",
       "      <td>7752.000000</td>\n",
       "      <td>7725.000000</td>\n",
       "      <td>7725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>22.932220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.211568</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>...</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>2.487613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>11.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>21.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>23.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>24.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>98.524734</td>\n",
       "      <td>100.000153</td>\n",
       "      <td>38.542255</td>\n",
       "      <td>29.619342</td>\n",
       "      <td>21.857621</td>\n",
       "      <td>213.414006</td>\n",
       "      <td>0.967277</td>\n",
       "      <td>...</td>\n",
       "      <td>21.621661</td>\n",
       "      <td>15.841235</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.645000</td>\n",
       "      <td>127.135000</td>\n",
       "      <td>212.335000</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           station  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "count  7750.000000   7682.000000   7682.000000  7677.000000  7677.000000   \n",
       "mean     13.000000     29.768211     23.225059    56.759372    88.374804   \n",
       "std       7.211568      2.969999      2.413961    14.668111     7.192004   \n",
       "min       1.000000     20.000000     11.300000    19.794666    58.936283   \n",
       "25%       7.000000     27.800000     21.700000    45.963543    84.222862   \n",
       "50%      13.000000     29.900000     23.400000    55.039024    89.793480   \n",
       "75%      19.000000     32.000000     24.900000    67.190056    93.743629   \n",
       "max      25.000000     37.600000     29.900000    98.524734   100.000153   \n",
       "\n",
       "       LDAPS_Tmax_lapse  LDAPS_Tmin_lapse     LDAPS_WS     LDAPS_LH  \\\n",
       "count       7677.000000       7677.000000  7677.000000  7677.000000   \n",
       "mean          29.613447         23.512589     7.097875    62.505019   \n",
       "std            2.947191          2.345347     2.183836    33.730589   \n",
       "min           17.624954         14.272646     2.882580   -13.603212   \n",
       "25%           27.673499         22.089739     5.678705    37.266753   \n",
       "50%           29.703426         23.760199     6.547470    56.865482   \n",
       "75%           31.710450         25.152909     8.032276    84.223616   \n",
       "max           38.542255         29.619342    21.857621   213.414006   \n",
       "\n",
       "         LDAPS_CC1  ...   LDAPS_PPT2   LDAPS_PPT3   LDAPS_PPT4          lat  \\\n",
       "count  7677.000000  ...  7677.000000  7677.000000  7677.000000  7752.000000   \n",
       "mean      0.368774  ...     0.485003     0.278200     0.269407    37.544722   \n",
       "std       0.262458  ...     1.762807     1.161809     1.206214     0.050352   \n",
       "min       0.000000  ...     0.000000     0.000000     0.000000    37.456200   \n",
       "25%       0.146654  ...     0.000000     0.000000     0.000000    37.510200   \n",
       "50%       0.315697  ...     0.000000     0.000000     0.000000    37.550700   \n",
       "75%       0.575489  ...     0.018364     0.007896     0.000041    37.577600   \n",
       "max       0.967277  ...    21.621661    15.841235    16.655469    37.645000   \n",
       "\n",
       "               lon          DEM        Slope  Solar radiation    Next_Tmax  \\\n",
       "count  7752.000000  7752.000000  7752.000000      7752.000000  7725.000000   \n",
       "mean    126.991397    61.867972     1.257048      5341.502803    30.274887   \n",
       "std       0.079435    54.279780     1.370444       429.158867     3.128010   \n",
       "min     126.826000    12.370000     0.098475      4329.520508    17.400000   \n",
       "25%     126.937000    28.700000     0.271300      4999.018555    28.200000   \n",
       "50%     126.995000    45.716000     0.618000      5436.345215    30.500000   \n",
       "75%     127.042000    59.832400     1.767800      5728.316406    32.600000   \n",
       "max     127.135000   212.335000     5.178230      5992.895996    38.900000   \n",
       "\n",
       "         Next_Tmin  \n",
       "count  7725.000000  \n",
       "mean     22.932220  \n",
       "std       2.487613  \n",
       "min      11.300000  \n",
       "25%      21.300000  \n",
       "50%      23.100000  \n",
       "75%      24.600000  \n",
       "max      29.800000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      "station             7750 non-null float64\n",
      "Date                7750 non-null object\n",
      "Present_Tmax        7682 non-null float64\n",
      "Present_Tmin        7682 non-null float64\n",
      "LDAPS_RHmin         7677 non-null float64\n",
      "LDAPS_RHmax         7677 non-null float64\n",
      "LDAPS_Tmax_lapse    7677 non-null float64\n",
      "LDAPS_Tmin_lapse    7677 non-null float64\n",
      "LDAPS_WS            7677 non-null float64\n",
      "LDAPS_LH            7677 non-null float64\n",
      "LDAPS_CC1           7677 non-null float64\n",
      "LDAPS_CC2           7677 non-null float64\n",
      "LDAPS_CC3           7677 non-null float64\n",
      "LDAPS_CC4           7677 non-null float64\n",
      "LDAPS_PPT1          7677 non-null float64\n",
      "LDAPS_PPT2          7677 non-null float64\n",
      "LDAPS_PPT3          7677 non-null float64\n",
      "LDAPS_PPT4          7677 non-null float64\n",
      "lat                 7752 non-null float64\n",
      "lon                 7752 non-null float64\n",
      "DEM                 7752 non-null float64\n",
      "Slope               7752 non-null float64\n",
      "Solar radiation     7752 non-null float64\n",
      "Next_Tmax           7725 non-null float64\n",
      "Next_Tmin           7725 non-null float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出数据有缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isnull().sum()#查看每一行空值的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7588 entries, 0 to 7749\n",
      "Data columns (total 25 columns):\n",
      "station             7588 non-null float64\n",
      "Date                7588 non-null object\n",
      "Present_Tmax        7588 non-null float64\n",
      "Present_Tmin        7588 non-null float64\n",
      "LDAPS_RHmin         7588 non-null float64\n",
      "LDAPS_RHmax         7588 non-null float64\n",
      "LDAPS_Tmax_lapse    7588 non-null float64\n",
      "LDAPS_Tmin_lapse    7588 non-null float64\n",
      "LDAPS_WS            7588 non-null float64\n",
      "LDAPS_LH            7588 non-null float64\n",
      "LDAPS_CC1           7588 non-null float64\n",
      "LDAPS_CC2           7588 non-null float64\n",
      "LDAPS_CC3           7588 non-null float64\n",
      "LDAPS_CC4           7588 non-null float64\n",
      "LDAPS_PPT1          7588 non-null float64\n",
      "LDAPS_PPT2          7588 non-null float64\n",
      "LDAPS_PPT3          7588 non-null float64\n",
      "LDAPS_PPT4          7588 non-null float64\n",
      "lat                 7588 non-null float64\n",
      "lon                 7588 non-null float64\n",
      "DEM                 7588 non-null float64\n",
      "Slope               7588 non-null float64\n",
      "Solar radiation     7588 non-null float64\n",
      "Next_Tmax           7588 non-null float64\n",
      "Next_Tmin           7588 non-null float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "Data.dropna(axis=0, how='any', inplace=True)\n",
    "Data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成报告我这里电脑问题解决不了，问了老师也没解决，老师说先放着"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      "Present_Tmax        7752 non-null float64\n",
      "LDAPS_RHmax         7752 non-null float64\n",
      "LDAPS_Tmax_lapse    7752 non-null float64\n",
      "LDAPS_WS            7752 non-null float64\n",
      "LDAPS_LH            7752 non-null float64\n",
      "LDAPS_CC1           7752 non-null float64\n",
      "LDAPS_CC2           7752 non-null float64\n",
      "LDAPS_CC3           7752 non-null float64\n",
      "LDAPS_CC4           7752 non-null float64\n",
      "LDAPS_PPT1          7752 non-null float64\n",
      "LDAPS_PPT4          7752 non-null float64\n",
      "lat                 7752 non-null float64\n",
      "lon                 7752 non-null float64\n",
      "DEM                 7752 non-null float64\n",
      "Slope               7752 non-null float64\n",
      "Solar radiation     7752 non-null float64\n",
      "Next_Tmax           7752 non-null float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "df=Max.fillna(Max.mean())\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续特征值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_std = std.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.11500407,\n",
       "         1.51793488, -0.37628214],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ..., -0.54215762,\n",
       "         1.22994952,  0.07209725],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ..., -0.7231326 ,\n",
       "         1.21653443,  0.26425985],\n",
       "       ...,\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.71933797,\n",
       "        -2.0743251 , -0.79263444],\n",
       "       [-3.30412736, -4.11344274, -4.08785706, ..., -0.8454552 ,\n",
       "        -2.35821196, -4.12345278],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  2.86143459,\n",
       "         1.51793488,  2.76237361]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "      <td>-0.632499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "      <td>-0.536418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "      <td>-0.792634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>2.762374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205   0.296287  \n",
       "...        ...       ...       ...       ...              ...        ...  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  -0.632499  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  -0.536418  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  -0.792634  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935   2.762374  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_std_pf=pd.DataFrame(df_std,columns=['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax'])\n",
    "df_std_pf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_std_pf.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df_std_pf['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size=0.3,random_state=420)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性回归建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7539674802919669"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7548561002578512"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSE均方误差和RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5426,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对训练集做预测\n",
    "y_pred =lr.predict(Xtrain)#得到预测结果\n",
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5426, 16)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24617586900240698"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估训练集集合情况  参数1:真实标签 参数2:预测标签\n",
    "mean_squared_error(Ytrain,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24473776268159916"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = lr.predict(Xtest)\n",
    "TMSE=mean_squared_error(Ytest,y_test_pred)\n",
    "TMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49470977621389206"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRMSE=RMSE = np.sqrt(TMSE)\n",
    "TRMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16467f5cf60>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x164680647f0>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16467f57550>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(Ytrain)), Ytrain, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(Ytrain)), y_pred, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里有个问题，训练集和测试集分的比例不同，导致作图会出错，因为shape不同，不知道是不是matplotlib版本的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x164689bada0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 因为数据是无序的，所以画出的点是乱的\n",
    "plt.scatter(range(len(Ytest)),Ytest,s=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.21456121,  0.0515251 ,  0.61255723, -0.09355015,  0.06095759,\n",
       "       -0.08954571,  0.04337885, -0.05748748, -0.07158184,  0.01824843,\n",
       "       -0.00664616, -0.02581821, -0.05743904, -0.09232082,  0.09403802,\n",
       "        0.02192656])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用散点图观测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1646820acc0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "NameError",
     "evalue": "name 'xfit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-2addeb774251>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mYtrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxfit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myfit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;31m# plt.plot(range(len(y_test)), y_pred, 'b', label='Y-PREDICTED')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'xfit' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "plt.scatter(Ytrain,y_pred)\n",
    "plt.plot(xfit,yfit)\n",
    "# plt.plot(range(len(y_test)), y_pred, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(-4, 3, 100)\n",
    "xfit2 = xfit.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画辅助直线的时候有个版本问题，暂时还没办法解决"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xfit2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-48-aaebe2e6d033>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0myfit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxfit2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'xfit2' is not defined"
     ]
    }
   ],
   "source": [
    "yfit = lr.predict(xfit2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1328e647908>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1328e65f390>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13293eb1a90>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "plt.scatter(Ytrain,y_pred)\n",
    "plt.scatter(x1,y1,color='red', linewidth=10.0, linestyle='--')\n",
    "# plt.plot(range(len(y_test)), y_pred, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr =df_std_pf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax= plt.subplots(figsize = (14, 10))\n",
    "sns.heatmap(corr,cmap='RdBu', linewidths = 0.05,ax=ax,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.752000e+03\n",
       "mean    -1.469298e-15\n",
       "std      1.000065e+00\n",
       "min     -4.123453e+00\n",
       "25%     -6.645260e-01\n",
       "50%      7.209725e-02\n",
       "75%      7.446663e-01\n",
       "max      2.762374e+00\n",
       "Name: Next_Tmax, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_std_pf['Next_Tmax'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=df_std_pf['Next_Tmax'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.75200000e+03, -1.46929825e-15,  1.00006451e+00, -4.12345278e+00,\n",
       "       -6.64526037e-01,  7.20972505e-02,  7.44666339e-01,  2.76237361e+00])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.963317282918451"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR=t[7]-t[5]\n",
    "low_whisker=t[5]- IQR*1.5\n",
    "low_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除掉下边缘的记录\n",
    "df_std_pf[df_std_pf['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6175, 7750], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=df_std_pf[df_std_pf['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了两行\n",
    "X\n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.22952887192574323\n",
      "RMSE: 0.4790917155678474\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE =mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "原来MSE：0.24285429719701013\n",
    "原来的RMSE：0.49280249309130947\n",
    "成功降低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7455182172970509, 0.759486375370554)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) \n",
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n",
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388376\n",
      "RMSE: 0.4841852350845529\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
       "      <td>-0.203376</td>\n",
       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "             7          8         9        10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  \n",
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
       "      <td>0.259139</td>\n",
       "      <td>-0.363300</td>\n",
       "      <td>0.214156</td>\n",
       "      <td>-0.098169</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.420652</td>\n",
       "      <td>0.400378</td>\n",
       "      <td>0.343841</td>\n",
       "      <td>0.192053</td>\n",
       "      <td>0.162411</td>\n",
       "      <td>0.025265</td>\n",
       "      <td>-0.008065</td>\n",
       "      <td>0.076506</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
       "      <td>0.459231</td>\n",
       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0  -0.228159  0.259139 -0.363300  0.214156 -0.098169  0.389183  0.420652   \n",
       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "           7         8         9        10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535957, 0.7383072663535957)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) \n",
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) \n",
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=100,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(regressor, X, Y, cv=10,scoring = \"neg_mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(sklearn.metrics.SCORERS.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traing Score:0.975529\n",
      "Testing Score:0.820553\n"
     ]
    }
   ],
   "source": [
    "regr=RandomForestRegressor()\n",
    "regr.fit(Xtrain,Ytrain)\n",
    "print(\"Traing Score:%f\"%regr.score(Xtrain,Ytrain))\n",
    "print(\"Testing Score:%f\"%regr.score(Xtest,Ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不难看出随机森林回归算法得分比线性回归高，更适合在这种情况使用"
   ]
  }
 ],
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